Threat Behavior Detection uses AI to identify anomalous endpoint and network behaviours indicative of active threats, enabling earlier attack detection and containment, by modelling baseline user and system behaviour and flagging statistical deviations in real time, across security operations centre monitoring workflows.
Security operations teams relying on signature-based detection miss novel attack techniques and insider threat patterns that do not match known indicators of compromise, discovering breaches only after significant dwell time.
A behavioural anomaly model establishes baseline profiles for user activity, network traffic, and endpoint telemetry, detects deviations indicative of credential abuse, lateral movement, and data exfiltration, and generates ranked alerts for SOC analyst investigation.
Reduction in mean time to detect security incidents and reduction in false-positive alert volume versus signature-only detection.
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